Employing deep learning for sex estimation of adult individuals using 2D images of the humerus

Abstract

Biological profile estimation, of which sex estimation is a fundamental first stage, is a really important task in forensic human identification. Although there are a large number of methods that address this problem from different bone structures, mainly using the pelvis and the skull, it has been shown that the humerus presents significant sexual dimorphisms that can be used to estimate sex in their absence. However, these methods are often too subjective or costly, and the development of new methods that avoid these problems is one of the priorities in forensic anthropology research. In this respect, the use of artificial intelligence may allow to automate and reduce the subjectivity of biological profile estimation methods. In fact, artificial intelligence has been successfully applied in sex estimation tasks, but most of the previous work focuses on the analysis of the pelvis and the skull. More importantly, the humerus, which can be useful in some situations due to its resistance, has never been used in the development of an automatic sex estimation method. Therefore, this paper addresses the use of machine learning techniques to the task of image classification, focusing on the use of images of the distal epiphysis of the humerus to classify whether it belongs to a male or female individual. To address this, we have used a set of humerus photographs of 417 adult individuals of Mediterranean origin to validate and compare different approaches, using both deep learning and traditional feature extraction techniques. Our best model obtains an accuracy of 91.03% in test, correctly estimating the sex of 92.68% of the males and 89.19% of the females. These results are superior to the ones obtained by the state of the art and by a human expert, who has achieved an accuracy of 83.33% using a state-ofthe- art method on the same data. In addition, the visualization of activation maps allows us to confirm not only that the neural network observes the sexual dimorphisms that have been proposed by the forensic anthropology literature, but also that it has been capable of finding a new region of interest.